Time-lapse, 3D imaging of functional neural networks, composed of many neurons connected through a complex web of synapses, is a promising approach for gaining in-depth understanding of how the central nervous system (CNS) works. Using high speed confocal and fluorescence microscopy, 3D sequences are routinely acquired to elucidate the development of functional circuits, as well as the molecular kinetics and interactions that drive CNS development or pathological degeneration. It is now possible to image more complex and intact neural circuits in the CNS in situ with high lateral and axial resolution. Imaging of these new model systems could unleash a new generation of scientific inquiries that would lead to new discoveries and therapies. However, 3D neuronal sequences have a lower signal to noise ratio (SNR) while the complexity of particle motion is exacerbated by the more physiological environment. Therefore, quantification of particle dynamics and molecular interactions in these complex models is difficult due to the limitations of the current 3D image analysis tools. In particular, current particle tracking tools struggle to address these twin challenges. This constitutes a critical bottleneck and rate-limiting step for quantitative analysis of the biological mechanisms that underlie neural development and disease. We have developed a high performance and configurable tracking tool, well suited for a broad range of 2D particle tracking applications, which is now being commercialized by Nikon Corp. In a benchmark study covering broad particle tracking applications this tracking tool achieved significantly better performance than several commercial and academic tools (Table 1.I). Our collaborators at the Harvard Medical School are leaders in the field of neural development and synaptic morphogenesis. They routinely acquire high resolution, 3D confocal, in vitro imaging data showing microtubule dynamics and neuronal process morphometry using both vertebrate and invertebrate cells. This provide an excellent test platform for the next generation 3D tracking tool. The objective of this Phase I proposal is to develop and validate an informatics tool optimized for 3D subcellular tracking applications. The general purpose tool would address the challenge of detecting and tracking moving particles with heterogeneous motion in functional neural networks. These types of complex experimental preparations are increasingly being adopted and are drawing attention to the limitations of the current generation of tracking tools. The key innovations of the proposed tool include: 1) a Dynamic model and adaptive control that represents dynamic object states and transitions, and executes state-dependent particle detection and tracking methods; 2) Self-regulation of valid state transitions and track matching using motion energy (an independent check on the matching outcomes). We'll prove the feasibility in Phase I using intact preparations from Drosophila and Xenopus as well as simulated data. In Phase II we will tackle a broader set of 3D particle tracking applications, and also broadly address the market requirement for 3D kinetic microscopy informatics including 3D kinetic event characterization and screening. The specific aims are: Aim 1: Create and validate the 3D heterogeneous tracking tool using simulated 3D images Aim 2: Validate the tool in broad fluorescence 3D kinetic microscopy applications Aim 3: Execute a proof-of-principle experiment in +TIP tracking for functional neural networks